Neural Domain Adaptation for Biomedical Question Answering
Georg K. Wiese, Dirk Weissenborn, Mariana Neves · 2017
Factoid question answering (QA) has recently benefited from the development of deep learning (DL) systems.Neural network models outperform traditional approaches in domains where large datasets exist, such as SQuAD (≈ 100, 000 questions) for Wikipedia articles.However, these systems have not yet been applied to QA in more specific domains, such as biomedicine, because datasets are generally too small to train a DL system from scratch.For example, the BioASQ dataset for biomedical QA comprises less then 900 factoid (single answer) and list (multiple answers) QA instances.In this work, we adapt a neural QA system trained on a large open-domain dataset (SQuAD, source) to a biomedical dataset (BioASQ, target) by employing various transfer learning techniques.Our network architecture is based on a state-of-theart QA system, extended with biomedical word embeddings and a novel mechanism to answer list questions.In contrast to existing biomedical QA systems, our system does not rely on domain-specific ontologies, parsers or entity taggers, which are expensive to create.Despite this fact, our systems achieve state-of-the-art results on factoid questions and competitive results on list questions.